We’re excited about the kick-off of the last round of selected experiments from EUH4D!
While we update the website to display all the details, here’s a walkthrough of the experiments and the SMEs running them.
Syntra, run by NovelSense UG, proposes a unique and automated process to generate synthetic traffic datasets. These datasets can be used in the training, testing and benchmarking AI-based traffic monitoring systems. The objective is to contribute significantly to the availability of high-quality traffic data, ultimately enabling safe autonomous driving.
Arboria, run by Fora Forest Technologies, SLL, is a digital twin for urban trees that combines computer vision and machine learning to automate the inventory of street trees. In addition, it provides geo-information by leveraging and integrating data from airborne LiDAR, aerial orthoimages and GSV. Arboria is based on the results of Förecast 2.0.
Datman, from E-Stream, responds to the need of companies to monitor home-work travel habits and patterns of their employees, to design and put in place measures that reduce private vehicles use. On the other hand, the collected data will help competent territorial entities oversee and coordinate companies’ efforts.
Ferment AI, a project from Sonicat Systems SL, targets the quality and production departments of food manufacturing companies dealing with a wide range of fermentative processes, such as wine, beer, bread, and cheese, among others. In addition, the results will help other companies start their development path for alternative protein production in bioreactors using mass fermentation or precision fermentation with yeast or bacteria.
Ginger Bee, run by Proventus d.o.o, revolves around the digital gardening assistant Tomappo, a mobile and web application helping people, hobby gardeners grow their vegetables and garden centres, and gardening brands grow their businesses. The expected scenario is to build a knowledge graph that would enable us to link data generated by Tomappo users to external sources to cater better for the users’ needs with personalized and contextually relevant data and increase the business KPIs of Tomappo.
WASH AI, from Flex BI SIA, wants to apply AI, Machine learning prediction algorithms and Image detection services into self-service carwash business units to improve the efficiency of predictability of their operations.
AI4REHAB, from Blautic Designs, uses electronic wearables with ECG/EMG and 6-axis movement sensors along with intelligent textile garments with conductive electrodes to control and monitor the performance of exercises that involve muscle activity and movement, with the overall aim of the practices being observed and receiving feedback in real-time.
OVEC, run by Pumacy GmbH, will aid the EU requirement to inform citizens about potential risks within consumer products, specifically about vehicles. These products are under notable observation, and there are many sources for European citizens regarding information about their cars, production, and subsequent use. However, this information is not necessarily trustworthy. There is hardly any authorized data for later use, especially regarding security and quality.
VINEPRODATA, a project from Elmibit d.o.o., allows winegrowing organizations to improve their operations by precise timing of pesticide applications, fertilization, control of irrigation, work management and integration with precision farming technologies.
AICH, from Latitudo 40 SRL, is an innovative platform that enables carbon credit trading using an innovative deep learning algorithm to estimate aboveground forest biomass (AGB) accurately. The main innovation of the analysis model is to minimize ground data acquisition, using available datasets to validate the results and improve the analysis performance. Another innovative aspect is registering carbon credits generated by a forest on the blockchain to ensure transparency between actors.
IoT-DiTwiCS, an experiment from Binare Oy, will produce novel datasets/APIs/tech to enable the evaluation of connected devices from cybersecurity and functional perspectives via “Digital Twin” innovations to guarantee operational and cybersecurity compliance. It builds on the results of the successful IoT-SESOD experiment.
SBKN, from SaveBiking SRL Uninominale, wants to quantify how much low car use decreases road accidents, eventually favouring micro-mobility. Working with insurance companies to attract users through a mobility app that rewards users for sustainable mobility, low-risk users can buy discounted mobility insurance coverages. The experiment is based on the assumption that a low-risk frequent driver has less probability of causing a car accident and, similarly, that a micro-mobility user (someone using a bicycle, moped or walking) has less likelihood of causing a car accident when driving because they have a better perception of the risks. In addition, they are sensitive to the “weak” movers on the road.
Data-driven Hotel, run by AdQuiver Media S.L., will work on three use cases for hotel clients based on a dynamic booking and income predictor, a probabilistic cancellation classifier, and an unsupervised demand clustering. This should help hotels to identify different tourist profiles according to their needs and behaviour to obtain valuable insights that will allow them to prioritise segments and personalise their offer.
ProcessHealthCheck, from Nissatech, will develop a service for performing process behaviour analysis based on past process data. It can be used as a self-assessment test by manufacturing SMEs in their transformation in a data-driven economy. Nissatech will demonstrate the value of such a service for understanding how to process anomalies/instabilities that influence its environmental impact and how it can be improved, leading to zero defects and zero waste production.
OSM2TRAMOD, by RoadTwin sro, will develop a set of tools (OSM2TraMod) for the automated creation of a traffic model for any given city based on OpenStreetMaps.
R-ABLE, by WIZIPISI, aims to deliver data on the current state of arable lands, the dynamics of their share and analytical reports to bridge the gap in data acquisition and exchange between stakeholders at various levels of operations and background.
Waterports, from Prodevelop SL, focuses on water quality monitoring to help Port Authorities to control the marine abiotic capital to assess the impact on the marine ecosystem.
SODECAMA, from sovity GmbHm, will develop an application to detect shortages in production chains based on a data feed from different data sources and partners in a Data Space. The experiment contains the setup of a Data Space between various data providers and consumers, the successful provision of data via it to the developed application, which displays the relevant result (shortages) compliant with the IDS standard and remaining data sovereignty.